#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Algorithms.DataAnalysis;
using HeuristicLab.Data;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.Instances.DataAnalysis {
  public class GaussianProcess2dPeriodic : ArtificialRegressionDataDescriptor {

    public override string Name {
      get {
        return "Gaussian Process 2d periodic";
      }
    }
    public override string Description {
      get { return ""; }
    }
    protected override string TargetVariable { get { return "Y"; } }
    protected override string[] VariableNames { get { return new string[] { "X1", "X2", "Y" }; } }
    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
    protected override int TrainingPartitionStart { get { return 0; } }
    protected override int TrainingPartitionEnd { get { return 20 * 20; } }
    protected override int TestPartitionStart { get { return 20 * 20; } }
    protected override int TestPartitionEnd { get { return 2 * (20 * 20); } }

    protected override List<List<double>> GenerateValues() {
      List<List<double>> independentTrainingData = new List<List<double>>();
      List<List<double>> independentTestData = new List<List<double>>();
      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
        independentTrainingData.Add(ValueGenerator.GenerateSteps(0, 0.99, 1.0 / 20).ToList());
        independentTestData.Add(ValueGenerator.GenerateSteps(0.005, 1, 1.0 / 20).ToList());
      }


      var trainingData = ValueGenerator.GenerateAllCombinationsOfValuesInLists(independentTrainingData);
      var testData = ValueGenerator.GenerateAllCombinationsOfValuesInLists(independentTestData);
      List<List<double>> data = new List<List<double>>();
      foreach (var e in trainingData) {
        data.Add(e.ToList());
      }
      int j = 0;
      foreach (var e in testData) {
        data[j].AddRange(e);
        j++;
      }

      var covarianceFunction = new CovarianceSum();
      var m1 = new CovarianceMask();
      m1.SelectedDimensionsParameter.Value = new IntArray(new int[] { 0 });
      m1.CovarianceFunctionParameter.Value = new CovariancePeriodic();
      var m2 = new CovarianceMask();
      m2.SelectedDimensionsParameter.Value = new IntArray(new int[] { 1 });
      m2.CovarianceFunctionParameter.Value = new CovariancePeriodic();

      covarianceFunction.Terms.Add(m1);
      covarianceFunction.Terms.Add(m2);
      covarianceFunction.Terms.Add(new CovarianceNoise());
      var cov =
        covarianceFunction.GetParameterizedCovarianceFunction(
          Enumerable.Repeat(0.0, covarianceFunction.GetNumberOfParameters(2) - 1)
          .Concat(new double[] { Math.Log(Math.Sqrt(0.01)) })
          .ToArray(),
          new int[] { 0, 1});

      var mt = new MersenneTwister(31415);
      var target = Util.SampleGaussianProcess(mt, cov, data);
      data.Add(target);

      return data;
    }
  }
}